Full-Stack AI Developer

Role: Full Stack AI Engineer
Location: Charlotte NC (hybrid)
Employment Type: Fulltime
 
Position Summary:

• We are seeking a Full Stack AI Engineer who combines strong software engineering fundamentals with applied AI creativity. This role plays a foundational part in shaping the company’s AI and automation strategy—architecting, building, and deploying intelligent tools that transform how the business operates.
• You will own full-stack development of AI-driven internal tools, partner directly with operators and business units, and help build an internal culture of AI adoption. This role requires high ownership, strong execution, and a passion for building practical, real-world AI solutions.

• 4–7+ years of professional experience in software engineering with modern web frameworks.
• Strong Python experience in production environments.
• Experience shipping applied LLM features into production (not just prototypes), including summarization, extraction, classification, routing, or operator-assist workflows.
• Experience building RAG systems end-to-end including document ingestion pipelines, chunking strategy iteration, retrieval, and context assembly.
• Experience building agent or tool-calling workflows where models trigger tools or actions with clear contracts and safety boundaries.
• Experience delivering software used by real operators including review flows, exception handling, auditability, and measurable improvement.
• Strong communication and problem translation skills with the ability to turn ambiguous operational needs into deployable workflows with clear success metrics.
• Experience integrating with call center or transcript systems is preferred.
• Experience integrating with CRM or ERP platforms is preferred.
• Experience with evaluation practices for LLM systems such as sampling strategies, rubric-based scoring, regression checks, and prompt versioning is preferred.
• Strong REST API design experience, with versioning best practices preferred.
• Experience with asynchronous processing and pipeline-style workloads.
• Experience building internal or administrative user interfaces (React experience preferred).
• Familiarity with cloud deployment, CI/CD pipelines, and environment configuration.

• Design, prototype, and deploy full-stack AI applications using Python frameworks (FastAPI/Django) and modern front-end frameworks (React/Next.js).
• Develop AI-powered capabilities including summarization, classification, routing, extraction, and workflow automation.
• Build operator-facing tools such as review queues, exception handling, and traceability views to ensure AI outputs are trusted and usable.

• Design and implement reliable retrieval-augmented generation (RAG) systems including ingestion pipelines, embeddings, vector search, and context assembly.
• Build scalable patterns for APIs, cloud deployment, CI/CD, and AI service orchestration.
• Anticipate and mitigate common LLM failure modes including irrelevant retrieval, missing context, hallucinated outputs, and stale data.

• Identify manual workflows across operations, sales, finance, and field teams and replace them with AI-enabled automation.

• ingestion → retrieval → agent/tool execution → human review → measurement
• Design human-in-the-loop workflows with clear escalation paths, editable outputs, and feedback capture.

• Build integrations with operational systems including ServiceTitan, call center platforms, and internal data systems.
• Develop transcript-driven workflows such as call summaries, lead qualification insights, automated tagging, and routing.
• Ensure workflows operate reliably within real-world constraints including messy data, timing dependencies, and operational handoffs.

• Develop agent-based workflows that trigger tools to perform operational actions such as updating records, retrieving context, or triggering workflows.
• Implement safe tool-calling patterns including defined input/output contracts, validation, retries, and permission scoping.
• Ensure systems remain observable through logging, intermediate traces, and measurable outcomes.

• Partner with operators, engineering teams, and leadership to identify high-impact AI opportunities.
• Translate ambiguous business problems into deployable AI workflows.
• Communicate technical concepts clearly to non-technical stakeholders and support AI adoption across the organization.